The ability to explain how to solve a task allows humans to share learnt knowledge and speed up the collective learning process. A naive approach to generating an explanation would be to recall every decision made during the task. However, this is often undesirable because it leads to prohibitively long and complex explanations that cannot be easily understood by the recipient. It is therefore crucial that any explanation generating process is able to identify a small subset of key decisions that are fundamental for solving the task [1]}. In the social sciences this is referred to as explanation selection and refers to the fact that human explanations are biased to only a few important events or causes [2]}. Current approaches to generating explanations in Deep RL algorithms typically operate at the level of individual decisions, for example by computing saliency scores for all input features [3]}, [4]}, [5]}. They therefore do not produce selective task-level explanations and fundamentally lack selectivity in their explanations [6]}.
